Innovative Design of Marketing Ecosystems by А. I. Vasiliev, I. А. Brusakova

“…The formation and use of marketing ecosystems can simplify both the processes of transformation of technological patterns and the interaction of different types of business models of enterprises. …”
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The use of artificial intelligence to analyze and optimize financial flows by М. А. Miakisheva

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AI-Driven Optimization of Blockchain Scalability, Security, and Privacy Protection by Fujiang Yuan, Zihao Zuo, Yang Jiang, Wenzhou Shu, Zhen Tian, Chenxi Ye, Junye Yang, Zebing Mao, Xia Huang, Shaojie Gu, Yanhong Peng

“…Artificial intelligence, with its powerful data processing capability, pattern recognition ability, and adaptive optimization algorithms, can improve the transaction processing efficiency of blockchain, enhance the security mechanism, and optimize the privacy protection strategy, thus effectively alleviating the limitations of blockchain in terms of scalability and security. …”
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Explainable MRI-Based Ensemble Learnable Architecture for Alzheimer’s Disease Detection by Opeyemi Taiwo Adeniran, Blessing Ojeme, Temitope Ezekiel Ajibola, Ojonugwa Oluwafemi Ejiga Peter, Abiola Olayinka Ajala, Md Mahmudur Rahman, Fahmi Khalifa

“…With the advancements in deep learning methods, AI systems now perform at the same or higher level than human intelligence in many complex real-world problems. The data and algorithmic opacity of deep learning models, however, make the task of comprehending the input data information, the model, and model’s decisions quite challenging. …”
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Simulation of multicentric destruction with regard for inhomogeneous distribution of rated voltage by Vitaly F. Lukyanov, Semen S. Assaulenko

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Forecasting Delivery Time of Goods in Supply Chains Using Machine Learning Methods by V. K. Rezvanov, O. M. Romakina, E. V. Zaytseva

“…The presented study aims to fill these gaps and demonstrate the efficiency of using open, accessible data and known algorithms. The research objective is to describe a pattern of appropriate selection of the least resource-intensive delivery forecasting model based on the analysis of machine learning algorithms.Materials and Methods. …”
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Seasonal variability of SST fronts in the southern ocean south of 60°S: the relationship with sea ice by Linhong Zhao, Jingjing Zhang, Dingtian Yang, Shuqun Cai, Rong Zhong

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Brown adipose tissue machine learning nnU-Net V2 network using TriDFusion (3DF) by Daniel Lafontaine, Stephanie Chahwan, Gustavo Barraza, Burcin Agridag Ucpinar, Gunjan Kayal, Nicolás Gómez-Banoy, Paul Cohen, John L. Humm, Heiko Schöder

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Effective temperature in approximate quantum many-body states by Yu-Qin Chen, Shi-Xin Zhang

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Few-shot learning for novel object detection in autonomous driving by Yifan Zhuang, Pei Liu, Hao Yang, Kai Zhang, Yinhai Wang, Ziyuan Pu

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Machine Learning Creates a Simple Endoscopic Classification System that Improves Dysplasia Detection in Barrett’s Oesophagus amongst Non-expert Endoscopists by Vinay Sehgal, Avi Rosenfeld, David G. Graham, Gideon Lipman, Raf Bisschops, Krish Ragunath, Manuel Rodriguez-Justo, Marco Novelli, Matthew R. Banks, Rehan J. Haidry, Laurence B. Lovat

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Diagnostic performance of actigraphy in Alzheimer’s disease using a machine learning classifier – a cross-sectional memory clinic study by Mathias Holsey Gramkow, Andreas Brink-Kjær, Frederikke Kragh Clemmensen, Nikolai Sulkjær Sjælland, Gunhild Waldemar, Poul Jennum, Steen Gregers Hasselbalch, Kristian Steen Frederiksen

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Investigating potential biomarkers associated with antineutrophil cytoplasmic antibody (ANCA)-associated vasculitis using Mendelian randomization and transcriptomic analysis by Yujia Wang, Zhimin Chen, Kaiqi Huang, Keng Ye, Shiwei He, Yanfang Xu, Hong Chen

“…Early identification and accurate diagnosis of AAV is crucial for improving prognosis. However, research on biomarkers associated with AAV is limited. …”
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Transcriptome Classification Reveals Molecular Subgroups in Idiopathic Pulmonary Fibrosis by Yuxia Liu, Chang Xu, Wenxin Gao, Huaqiong Liu, Chenglong Li, Mingwei Chen

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Characterizing individual and methodological risk factors for survey non-completion using machine learning: findings from the U.S. Millennium Cohort Study by Nate C. Carnes, Claire A. Kolaja, Crystal L. Lewis, Sheila F. Castañeda, Rudolph P. Rull, for the Millennium Cohort Study Team

“…Respondent characteristics and survey attributes may contribute to patterns of survey non-completion, a form of missing data in which respondents begin but do not finish a survey, that can lead to biased conclusions. …”
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An Evaluation of the Use of Simulated Annealing to Optimize Thinning Rates for Single Even-Aged Stands by Kai Moriguchi, Tatsuhito Ueki, Masashi Saito

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Visualizing leadership classifications in rectangular data using a basket model and co-word network analysis: a case study of U.S. HCAHPS survey results by Tsair-Wei Chien, Willy Chou

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Examining perinatal regionalization in practice: a network analysis of maternal transport in Georgia by Jingyu Li, Stephanie M. Radke, Lauren N. Steimle

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Examining prospective mathematics teachers’ computational thinking through the lens of cognitive style by Swasti Maharani, Ardiana Ardiana, Tri Andari

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Dynamic pricing with waiting and price-anticipating customers by Fabian Lange, Rainer Schlosser

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